| Researchers have demonstrated a computerized process that utilizes synthetic thinking in addition to cutting-edge image processing to help easily look at large numbers of individual Caenorhabditis elegans, a common species of nematode trusted for biological research. Beyond replacing manual assessments using applying microfluidics and computerized equipment, the particular system's power to diagnose subtle differences via worm-to-worm – without human involvement – may identify ancestral mutations that might possibly not have been found otherwise. |
esearch into the genetic factors behind certain disease mechanisms, illness progression and response to new drugs is frequently carried out using tiny multi-cellular animals such as nematodes, fruit flies or zebra fish.
Often, progress relies on the microscopic visual examination of many individual animals to detect mutants worthy of further study.
Now, scientists have demonstrated a computerized process that utilizes synthetic thinking in addition to cutting-edge image processing to help easily look at large numbers of individual Caenorhabditis elegans, a common species of nematode trusted for biological research. Beyond replacing manual assessments using applying microfluidics and computerized equipment, the particular system's power to diagnose subtle differences via worm-to-worm – without human involvement – may identify ancestral mutations that might possibly not have been found otherwise.
By making it possible for a huge number of worms to be analyzed autonomously in a small fraction of times necessary for traditional methods, the technique could change the way in which that large throughput ancestral screening process is done with C. elegans.
By allowing thousands of worms to be examined autonomously in a fraction of the time required for conventional manual screening, the technique could change the way that high throughput genetic screening is carried out using C. elegans.
Details of the research were scheduled to be reported August 19th in the advance online publication of the journal Nature Methods. The research has been supported by the National Institutes of Health (NIH), the National Science Foundation (NSF) and the Alfred P. Sloan Foundation.
"While humans are very good at pattern recognition, computers are much better than humans at detecting subtle differences, such as small changes in the location of dots or slight variations in the brightness of an image," said Hang Lu, the project's lead researcher and an associate professor in the School of Chemical & Biomolecular Engineering at the Georgia Institute of Technology. "This technique found differences that would have been almost impossible to pick out by hand."
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| Georgia Tech associate professor Hang Lu holds a microfluidic chip that is part of a system used to automatically examine large number of nematodes used for genetic research. Image Source: Gary Meek |
Because of the large number of possible genes involved in these developmental processes, the researchers must examine thousands of worms – perhaps as many as 100,000 – to exhaust the search. Lu and her research group had earlier developed a microfluidic "worm sorter" that speeds up the process of examining worms under a microscope, but until now, there were two options for detecting the mutants: a human had to look at each animal, or a simple heuristic algorithm was used to make the sorting decision. Neither option is objective or adaptable to new problems.
Lu's system, an optimized version of earlier work by her group, uses a camera to record three-dimensional images of each worm as it passes through the sorter. The system compares each image set against what it has been taught the "wild type" worms should look like. Worms that are even subtly different from normal can be sorted out for further study.
"We feed the program wild-type images, and it teaches itself to recognize what differentiates the wild type. It uses this information to determine what a mutant type may look like – which is information we didn't provide to the system – and sorts the worms based on that," explained Matthew Crane, a graduate student who performed the work. "We don't have to show the computer every possible mutant, and that is very powerful. And the computer never gets bored."
Georgia Tech associate professor Hang Lu holds a microfluidic chip that is part of a system that uses artificial intelligence and cutting-edge image processing to automatically examine large number of nematodes used for genetic research. Image Source: Gary Meek
While the system was designed to sort C. elegans for a specific research project, Lu believes the machine learning technology – which is borrowed from computer science – could be applied to other areas of biology that use model genetic organisms. The system's hardware and software are currently being used in several other laboratories beyond Georgia Tech.
"Our automated technique can be generalized to anything that relies on detecting a morphometric – or shape, size or brightness difference," Lu said. "We can apply this to anything that can be detected visually, and we think this could be expanded to studying many other problems related to learning, memory, neuro-degeneration and neural developmental diseases that this worm can be used to model."
Individual C. elegans are less than a millimeter long and thinner than a strand of hair, but have 302 neurons with well-defined synapses - so much so that they are said to be the only organism with a defined connectome. While research using single cells can be simpler to do, studies using the worms are good in vivo models for many important processes relevant to human health.
"We are hoping that the technology will really change the approach people can take to this kind of research," said Lu. "We expect that this approach will enable people to do much larger scale experiments that can push the science forward beyond looking what individual mutations are doing in a specific situation."
SOURCE EurekAlert
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